
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go


pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)


a = pd.read_csv("C:/Users/KARTHIKEYANVS/Documents/IPL/IPL Ball-by-Ball 2008-2020.csv")
a.index = range(1,len(a)+1)
balls = a.copy()


b = pd.read_csv("C:/Users/KARTHIKEYANVS/Documents/IPL/IPL Matches 2008-2020.csv")
b.index = range(1,len(b)+1)
matches = b.copy()


balls.head()


matches.head()


balls.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 193468 entries, 1 to 193468
Data columns (total 18 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   id                193468 non-null  int64 
 1   inning            193468 non-null  int64 
 2   over              193468 non-null  int64 
 3   ball              193468 non-null  int64 
 4   batsman           193468 non-null  object
 5   non_striker       193468 non-null  object
 6   bowler            193468 non-null  object
 7   batsman_runs      193468 non-null  int64 
 8   extra_runs        193468 non-null  int64 
 9   total_runs        193468 non-null  int64 
 10  non_boundary      193468 non-null  int64 
 11  is_wicket         193468 non-null  int64 
 12  dismissal_kind    9495 non-null    object
 13  player_dismissed  9495 non-null    object
 14  fielder           6784 non-null    object
 15  extras_type       10233 non-null   object
 16  batting_team      193468 non-null  object
 17  bowling_team      193277 non-null  object
dtypes: int64(9), object(9)
memory usage: 26.6+ MB


matches.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 816 entries, 1 to 816
Data columns (total 17 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   id               816 non-null    int64  
 1   city             803 non-null    object 
 2   date             816 non-null    object 
 3   player_of_match  812 non-null    object 
 4   venue            816 non-null    object 
 5   neutral_venue    816 non-null    int64  
 6   team1            816 non-null    object 
 7   team2            816 non-null    object 
 8   toss_winner      816 non-null    object 
 9   toss_decision    816 non-null    object 
 10  winner           812 non-null    object 
 11  result           812 non-null    object 
 12  result_margin    799 non-null    float64
 13  eliminator       812 non-null    object 
 14  method           19 non-null     object 
 15  umpire1          816 non-null    object 
 16  umpire2          816 non-null    object 
dtypes: float64(1), int64(2), object(14)
memory usage: 108.5+ KB


balls.shape

(193468, 18)


matches.shape

(816, 17)


balls.size

3482424


matches.size

13872


Total_matches = matches.shape[0]
Total_matches

816


venue = matches.venue.unique()


pd.Series(venue)

0                                 M Chinnaswamy Stadium
1            Punjab Cricket Association Stadium, Mohali
2                                      Feroz Shah Kotla
3                                      Wankhede Stadium
4                                          Eden Gardens
5                                Sawai Mansingh Stadium
6             Rajiv Gandhi International Stadium, Uppal
7                       MA Chidambaram Stadium, Chepauk
8                            Dr DY Patil Sports Academy
9                                              Newlands
10                                     St George's Park
11                                            Kingsmead
12                                      SuperSport Park
13                                         Buffalo Park
14                                New Wanderers Stadium
15                                De Beers Diamond Oval
16                                      OUTsurance Oval
17                                    Brabourne Stadium
18                         Sardar Patel Stadium, Motera
19                                     Barabati Stadium
20         Vidarbha Cricket Association Stadium, Jamtha
21         Himachal Pradesh Cricket Association Stadium
22                                        Nehru Stadium
23                               Holkar Cricket Stadium
24    Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...
25                           Subrata Roy Sahara Stadium
26     Shaheed Veer Narayan Singh International Stadium
27                   JSCA International Stadium Complex
28                                 Sheikh Zayed Stadium
29                              Sharjah Cricket Stadium
30                  Dubai International Cricket Stadium
31              Maharashtra Cricket Association Stadium
32    Punjab Cricket Association IS Bindra Stadium, ...
33               Saurashtra Cricket Association Stadium
34                                           Green Park
35                                M.Chinnaswamy Stadium
dtype: object


len(venue)

36


team = matches.team1.unique()
pd.Series(team)

0     Royal Challengers Bangalore
1                 Kings XI Punjab
2                Delhi Daredevils
3                  Mumbai Indians
4           Kolkata Knight Riders
5                Rajasthan Royals
6                 Deccan Chargers
7             Chennai Super Kings
8            Kochi Tuskers Kerala
9                   Pune Warriors
10            Sunrisers Hyderabad
11                  Gujarat Lions
12        Rising Pune Supergiants
13         Rising Pune Supergiant
14                 Delhi Capitals
dtype: object


tw = matches.toss_winner.value_counts()
max_toss_winner = tw.idxmax()
max_toss_winner

'Mumbai Indians'


play_match = matches.player_of_match.value_counts()
Most_player_of_match = play_match.idxmax()
Most_player_of_match

'AB de Villiers'


sachin = balls[balls['batsman'] == 'SR Tendulkar']


Sachin_dismissal = sachin['dismissal_kind'].value_counts()
Sachin_dismissal

dismissal_kind
caught               36
bowled               12
run out               8
lbw                   8
stumped               3
caught and bowled     2
retired hurt          2
Name: count, dtype: int64


run_catgorised = sachin['batsman_runs'].value_counts()
run_catgorised

batsman_runs
0    874
1    721
4    296
2    117
6     29
3      7
Name: count, dtype: int64


single_run = len(sachin[sachin['batsman_runs']==1])
single_run

721


double_runs = len(sachin[sachin['batsman_runs']==2])*2
double_runs

234


triple =  len(sachin[sachin['batsman_runs']==3])*3
triple

21


fours = len(sachin[sachin['batsman_runs']==4])*4
fours

1184


sixes = len(sachin[sachin['batsman_runs']==6])*6
sixes

174


dot = len(sachin[sachin['batsman_runs']==0])
dot

874


labels = ["1's","2's","3's","4's","6's"]


values = [single_run,double_runs,triple,fours,sixes]
values

[721, 234, 21, 1184, 174]


total_run = sum(values)
total_run

2334


colors = ['blue', 'r', 'y', 'g','orange']  
aa = go.Pie(values = values,
      labels = labels,
      hole = 0.3,
           marker=dict(colors=colors))
fig = go.Figure(aa)
fig.update_layout(title_text="Sachin's Runs", title_x=0.5, title_y=0.9, title_font_size=20)
fig.show()


steyn = balls[balls['bowler'] == 'DW Steyn']
steyn_wickets = steyn[steyn['is_wicket']==1].dismissal_kind.value_counts()
steyn_wickets

dismissal_kind
caught               62
bowled               26
run out               8
lbw                   6
caught and bowled     2
hit wicket            1
Name: count, dtype: int64


len(steyn[(steyn['dismissal_kind']=='run out') & (steyn['fielder'] == 'DW Steyn')])

2


steyn_wickets['run out'] = 2
steyn_wickets

dismissal_kind
caught               62
bowled               26
run out               2
lbw                   6
caught and bowled     2
hit wicket            1
Name: count, dtype: int64


total_wickets_by_steyn = sum(steyn_wickets)
total_wickets_by_steyn

99


l_ste = steyn_wickets.index
val_ste = steyn_wickets.values
colors = ['blue', 'green', 'red', 'orange', 'yellow', 'cyan']


bar = go.Bar(x=l_ste, y=val_ste, marker=dict(color=colors), legendgroup='Steyn')

layout = go.Layout(
    title="Steyn's Wicket",
    title_x=0.5,
    title_y=0.9,
    xaxis=dict(title='Wickets'),
    yaxis=dict(title='Count'),
    barmode='group'  
)

fig = go.Figure(data=[bar], layout=layout)


fig.show()


matches.columns

Index(['id', 'city', 'date', 'player_of_match', 'venue', 'neutral_venue',
       'team1', 'team2', 'toss_winner', 'toss_decision', 'winner', 'result',
       'result_margin', 'eliminator', 'method', 'umpire1', 'umpire2'],
      dtype='object')


matches['date'] = pd.to_datetime(matches['date'])


matches['year'] = matches['date'].dt.year


matches['year'].value_counts()

year
2013    76
2012    74
2011    73
2010    60
2014    60
2016    60
2018    60
2019    60
2020    60
2015    59
2017    59
2008    58
2009    57
Name: count, dtype: int64


seasons = matches.groupby(['year','toss_decision']).size().reset_index()
seasons = seasons.rename(columns={0:'Count'})
seasons.index = range(1,len(seasons)+1)
seasons


import warnings

# Filter out FutureWarnings
warnings.filterwarnings("ignore")
plt.figure(figsize = (10,7))
sns.barplot(x='year',y = 'Count', hue = 'toss_decision', data = seasons)
plt.show()


matches.columns

Index(['id', 'city', 'date', 'player_of_match', 'venue', 'neutral_venue',
       'team1', 'team2', 'toss_winner', 'toss_decision', 'winner', 'result',
       'result_margin', 'eliminator', 'method', 'umpire1', 'umpire2', 'year'],
      dtype='object')


matches[['toss_winner','winner']]


matches['toss_game_winner'] = matches['toss_winner']==matches['winner']


matches['toss_game_winner'] = matches['toss_game_winner'].map({True:'Yes',
                                False:'No'})


matches[['toss_winner','winner','toss_game_winner']]


toss = matches['toss_game_winner'].value_counts()


toss = pd.Series(toss)


toss

toss_game_winner
Yes    418
No     398
Name: count, dtype: int64


l = toss.index
v = toss.values
Pie = go.Pie(labels = l,
      values = v,
            hole = 0.5)
layout = go.Layout(title = 'Toss Winning Implies Winning Game or Not',
                   title_x = 0.5, 
                   title_y = 0.9)
f = go.Figure(Pie,layout)
f.show()


matches[['year','winner']]


winner_13 = matches[matches['year']==2013].tail(1)


winner_13[['year','winner']]


final_matches = []

for a in matches['year'].unique():
    b = matches[matches['year']==a]
    final =  b.tail(1)
    final_matches.append(final)


final_matches = pd.concat(final_matches, ignore_index=True)


final_matches.index  = range(1,len(final_matches)+1)


final_matches.index.name = 'Season'


final_matches


winners_df = final_matches['winner'].value_counts()


x = winners_df.index
y = winners_df.values
colors = ['blue','yellow','red','pink','black','orange']
bar = go.Bar(x=x, y=y, marker=dict(color=colors))

layout = go.Layout(
    title='Champions Count',
    title_x=0.5,
    title_y=0.9,
    xaxis=dict(title='Teams'),
    yaxis=dict(title='Champions Count')
)


fig = go.Figure(bar)
fig.update_layout(layout)


fig.show()


matches['team1'].value_counts()

team1
Royal Challengers Bangalore    108
Mumbai Indians                  97
Kolkata Knight Riders           95
Chennai Super Kings             94
Kings XI Punjab                 92
Delhi Daredevils                83
Rajasthan Royals                70
Sunrisers Hyderabad             59
Deccan Chargers                 39
Pune Warriors                   23
Delhi Capitals                  19
Gujarat Lions                   16
Kochi Tuskers Kerala             7
Rising Pune Supergiants          7
Rising Pune Supergiant           7
Name: count, dtype: int64


match_played = matches['team1'].value_counts() + matches['team2'].value_counts()


match_play = match_played.to_frame().reset_index()


match_play.columns = ['Teams','Matches Played']


match_play


winners = matches['winner'].value_counts().to_frame()
winners = winners.reset_index()


winners.columns = ['Teams','Winning Count']


winners


team_analyse = match_play.merge(winners,
                                on = 'Teams',
                               how = 'inner')


team_analyse['lose'] = team_analyse['Matches Played'] - team_analyse['Winning Count']


team_analyse.sort_values(by = 'Winning Count',
                        ascending = False)


bar1 = go.Bar(
x = team_analyse['Teams'],
y = team_analyse['Matches Played'],
name = 'Total Matches')

bar2 = go.Bar(
x = team_analyse['Teams'],
y = team_analyse['Winning Count'],
name = 'Winning Count')

data = [bar1,bar2]

layout = go.Layout(
    title='Team Analysis',
    barmode='group',
    title_x = 0.5,
    title_y = 0.9,
    xaxis=dict(title='Teams'),
    yaxis=dict(title='Count')
)

# Create a figure and set the layout
fig = go.Figure(data=data, layout=layout)

fig.show()

	id	inning	over	ball	batsman	non_striker	bowler	batsman_runs	total_runs	dismissal_kind	player_dismissed	fielder	extras_type	batting_team	bowling_team
1	335982	1	6	5	RT Ponting	BB McCullum	AA Noffke	1	1	NaN	NaN	NaN	NaN	Kolkata Knight Riders	Royal Challengers Bangalore
2	335982	1	6	6	BB McCullum	RT Ponting	AA Noffke	1	1	NaN	NaN	NaN	NaN	Kolkata Knight Riders	Royal Challengers Bangalore
3	335982	1	7	1	BB McCullum	RT Ponting	Z Khan	0	0	NaN	NaN	NaN	NaN	Kolkata Knight Riders	Royal Challengers Bangalore
4	335982	1	7	2	BB McCullum	RT Ponting	Z Khan	1	1	NaN	NaN	NaN	NaN	Kolkata Knight Riders	Royal Challengers Bangalore
5	335982	1	7	3	RT Ponting	BB McCullum	Z Khan	1	1	NaN	NaN	NaN	NaN	Kolkata Knight Riders	Royal Challengers Bangalore

Data Analysis and Visualization using Python¶

Information about the Columns in Both Datasets¶

BASIC ANALYSIS¶

Single Player Analysis¶

Winning Toss Implies Winning Game or not¶

Tournaments Winners¶

	id	city	date	player_of_match	venue	team1	team2	toss_winner	toss_decision	winner	result	result_margin	eliminator	method	umpire1	umpire2
1	335982	Bangalore	2008-04-18	BB McCullum	M Chinnaswamy Stadium	Royal Challengers Bangalore	Kolkata Knight Riders	Royal Challengers Bangalore	field	Kolkata Knight Riders	runs	140.0	N	NaN	Asad Rauf	RE Koertzen
2	335983	Chandigarh	2008-04-19	MEK Hussey	Punjab Cricket Association Stadium, Mohali	Kings XI Punjab	Chennai Super Kings	Chennai Super Kings	bat	Chennai Super Kings	runs	33.0	N	NaN	MR Benson	SL Shastri
3	335984	Delhi	2008-04-19	MF Maharoof	Feroz Shah Kotla	Delhi Daredevils	Rajasthan Royals	Rajasthan Royals	bat	Delhi Daredevils	wickets	9.0	N	NaN	Aleem Dar	GA Pratapkumar
4	335985	Mumbai	2008-04-20	MV Boucher	Wankhede Stadium	Mumbai Indians	Royal Challengers Bangalore	Mumbai Indians	bat	Royal Challengers Bangalore	wickets	5.0	N	NaN	SJ Davis	DJ Harper
5	335986	Kolkata	2008-04-20	DJ Hussey	Eden Gardens	Kolkata Knight Riders	Deccan Chargers	Deccan Chargers	bat	Kolkata Knight Riders	wickets	5.0	N	NaN	BF Bowden	K Hariharan

	year	toss_decision	Count
1	2008	bat	26
2	2008	field	32
3	2009	bat	35
4	2009	field	22
5	2010	bat	39
6	2010	field	21
7	2011	bat	25
8	2011	field	48
9	2012	bat	37
10	2012	field	37
11	2013	bat	45
12	2013	field	31
13	2014	bat	19
14	2014	field	41
15	2015	bat	25
16	2015	field	34
17	2016	bat	11
18	2016	field	49
19	2017	bat	11
20	2017	field	48
21	2018	bat	10
22	2018	field	50
23	2019	bat	10
24	2019	field	50
25	2020	bat	27
26	2020	field	33

	id	city	date	player_of_match	venue	neutral_venue	team1	team2	toss_winner	toss_decision	winner	result	result_margin	eliminator	method	umpire1	umpire2	year	toss_game_winner
Season
1	336040	Mumbai	2008-06-01	YK Pathan	Dr DY Patil Sports Academy	0	Chennai Super Kings	Rajasthan Royals	Rajasthan Royals	field	Rajasthan Royals	wickets	3.0	N	NaN	BF Bowden	RE Koertzen	2008	Yes
2	392239	Johannesburg	2009-05-24	A Kumble	New Wanderers Stadium	1	Royal Challengers Bangalore	Deccan Chargers	Royal Challengers Bangalore	field	Deccan Chargers	runs	6.0	N	NaN	RE Koertzen	SJA Taufel	2009	No
3	419165	Mumbai	2010-04-25	SK Raina	Dr DY Patil Sports Academy	0	Chennai Super Kings	Mumbai Indians	Chennai Super Kings	bat	Chennai Super Kings	runs	22.0	N	NaN	RE Koertzen	SJA Taufel	2010	Yes
4	501271	Chennai	2011-05-28	M Vijay	MA Chidambaram Stadium, Chepauk	0	Chennai Super Kings	Royal Challengers Bangalore	Chennai Super Kings	bat	Chennai Super Kings	runs	58.0	N	NaN	Asad Rauf	SJA Taufel	2011	Yes
5	548381	Chennai	2012-05-27	MS Bisla	MA Chidambaram Stadium, Chepauk	0	Kolkata Knight Riders	Chennai Super Kings	Chennai Super Kings	bat	Kolkata Knight Riders	wickets	5.0	N	NaN	BF Bowden	SJA Taufel	2012	No
6	598073	Kolkata	2013-05-26	KA Pollard	Eden Gardens	0	Chennai Super Kings	Mumbai Indians	Mumbai Indians	bat	Mumbai Indians	runs	23.0	N	NaN	HDPK Dharmasena	SJA Taufel	2013	Yes
7	734049	Bangalore	2014-06-01	MK Pandey	M Chinnaswamy Stadium	0	Kolkata Knight Riders	Kings XI Punjab	Kolkata Knight Riders	field	Kolkata Knight Riders	wickets	3.0	N	NaN	HDPK Dharmasena	BNJ Oxenford	2014	Yes
8	829823	Kolkata	2015-05-24	RG Sharma	Eden Gardens	0	Mumbai Indians	Chennai Super Kings	Chennai Super Kings	field	Mumbai Indians	runs	41.0	N	NaN	HDPK Dharmasena	RK Illingworth	2015	No
9	981019	Bangalore	2016-05-29	BCJ Cutting	M Chinnaswamy Stadium	0	Royal Challengers Bangalore	Sunrisers Hyderabad	Sunrisers Hyderabad	bat	Sunrisers Hyderabad	runs	8.0	N	NaN	HDPK Dharmasena	BNJ Oxenford	2016	Yes
10	1082650	Hyderabad	2017-05-21	KH Pandya	Rajiv Gandhi International Stadium, Uppal	0	Mumbai Indians	Rising Pune Supergiant	Mumbai Indians	bat	Mumbai Indians	runs	1.0	N	NaN	NJ Llong	S Ravi	2017	Yes
11	1136620	Mumbai	2018-05-27	SR Watson	Wankhede Stadium	0	Chennai Super Kings	Sunrisers Hyderabad	Chennai Super Kings	field	Chennai Super Kings	wickets	8.0	N	NaN	M Erasmus	S Ravi	2018	Yes
12	1181768	Hyderabad	2019-05-12	JJ Bumrah	Rajiv Gandhi International Stadium, Uppal	0	Mumbai Indians	Chennai Super Kings	Mumbai Indians	bat	Mumbai Indians	runs	1.0	N	NaN	IJ Gould	Nitin Menon	2019	Yes
13	1237181	Dubai	2020-11-10	TA Boult	Dubai International Cricket Stadium	0	Delhi Capitals	Mumbai Indians	Delhi Capitals	bat	Mumbai Indians	wickets	5.0	N	NaN	CB Gaffaney	Nitin Menon	2020	No